Novel applications of Machine Learning to Network Traffic Analysis

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[11] Nguyen HM, Cooper EW, Kamei K (2011) Borderline over-sampling for imbalanced data classification. International Journal of Knowledge Engineering and Soft Data Paradigms. vol. 3. no. 1. pp. 4-21. [12] Batista G, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter. vol. 6. no. 1. pp. 20-29. [13] Cieslak DA, Chawla NV, Striegel A (2006) Combating imbalance in network intrusion datasets. IEEE International Conference on Granular Computing, pp. 732-737. [14] He H, Bai Y, Garcia EA et al (2008) ADASYN: Adaptive synthetic sampling approach for imbalanced learning. IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322-1328. [15] Liu XY, Wu J, Zhou ZH (2009) Exploratory Undersampling for Class-Imbalance Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 2, pp. 539-550. [16] Tavallaee M, Bagheri E, Lu W et al (2009) A Detailed Analysis of the KDD CUP 99 Data Set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009), pages 53-58. [17] Gregor K, Danihelka I, Graves A et al (2015) DRAW: A Recurrent Neural Network For Image Generation. arXiv:1502.04623 [cs.CV]. [18] Jang E, Gu Sh, Poole B (2016). Categorical Reparameterization with Gumbel-Softmax. arXiv:1611.01144v2 [stat.ML]. [19] An J, Cho S (2015) Variational Autoencoder based Anomaly Detection using Reconstruction Probability. SNU Data Mining Center, 2015-2 Special Lecture on IE [20] Bhuyan MH, Bhattacharyya DK, Kalita JK (2014) Network Anomaly Detection: Methods, Systems and Tools. IEEE Communications Survey & Tutorials, vol. 16, no. 1. [21] Sommer R, Paxson V (2010) Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. IEEE Symposium on Security and Privacy. [22] Ingre B, Yadav A (2015) Performance Analysis of NSL-KDD dataset using ANN. 2015 International Conference on Signal Processing and Communication Engineering Systems, Guntur, pp. 92-96. [23] Ibrahim LM, Basheer DT, Mahmod MS (2013) A comparison study for intrusion database (KDD99, NSL-KDD) based on self-organization map (SOM) artificial neural network. Journal of Engineering Science and Technology, vol. 8, no. 1 pp. 107-119, School of Engineering, Taylor’s University. [24] Hinton GE, Zemel RS (1993) Autoencoders, minimum description length and Helmholtz free energy. Proceedings of the 6th International Conference on Neural Information Processing Systems, pp. 3-10. [25] Bengio S, Bengio Y (2000) Taking on the curse of dimensionality in joint distributions using neural networks. IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 550-557. [26] Siracusa MR, Tieu K, Ihler AT et al (2005) Estimating dependency and significance for high-dimensional data. Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, vol. 5, pp. v/1085-v/1088. [27] Dhar SS, Chakraborty B, Chaudhuri P (2014) Comparison of multivariate distributions using quantile–quantile plots and related tests. arXiv:1407.1212 [math.ST]. [28] Burke MD (1977) On the multivariate two-sample problem using strong approximations of the EDF”. Journal of Multivariate Analysis. 7. pp. 491–511. [29] Justel A, Peña D, Zamar R (1997) A multivariate Kolmogorov–Smirnov test of goodness of fit. Statistics & Probability Letters, vol.35, Issue 3, pp. 251-259. [30] Tennekes M, Jonge E, Daas PJH (2013) Visualizing and Inspecting Large Datasets with Doctoral Thesis: Novel applications of Machine Learning to NTAP - 180

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